Evaluation of the definition of an eye iris image

ABSTRACT

A method and a system for evaluating the definition of the image of an eye iris or the like, consisting of approximately localizing the pupil in the image, defining, from the approximate position of the pupil, an examination window centered on this position, and applying a gradient accumulation operation to the luminance values of the pixels of the examination window, the running total being proportional to the definition score of the image.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention relates to the field of digital imageprocessing and, more specifically, to methods of identification orauthentication based on digital images of an eye.

[0003] 2. Discussion of the Related Art

[0004] Iris recognition is a well tested biometric identificationtechnique, provided that the image on which the analysis andidentification methods are applied is an exploitable image. Inparticular, the performance of recognition algorithms strongly dependson the definition of the image of the iris to be identified.

[0005] Now, in most applications, and especially in on-boardapplications (for example for an access control of a telephone or alaptop computer, for an electronic key, etc.), the used camera (digitalsensor and lens) does not have an autofocus system adjusting the (realor simulated) focal distance according to the distance.

[0006] Further, for obtaining a good resolution of the iris which onlytakes up a small surface area of the eye, the images are taken at arelatively short distance (generally on the order of from 10 to 30 cm).This results in a small field depth (distance range between the cameraand the eye in which the image is clear). This small field depth addedto the fact that the eye is spherical may even generate definitiondifferences between areas of a same eye image.

[0007] A processing previous to the actual iris recognition thusconsists of selecting a sufficiently clear image.

[0008] Generally, the shooting device takes a number of images rangingbetween 5 and 50 and a pre-processing system selects the image to besubmitted to the actual recognition algorithm.

[0009] The definition evaluation amounts to assigning, to each image, ascore characteristic of its definition. This enables either selecting asufficiently clear image with respect to a determined threshold, orselecting the clearest image among the images of a set. By convention,the higher the score assigned to an image, the clearer the image.

[0010] The present invention more specifically relates to thepreprocessing applied to images of the same eye to determine a scorecharacteristic of the definition of each image and, according to apreferred aspect, select that of these images which is the clearest.

[0011] Various techniques for evaluating the definition of digitalimages have already been provided, be it based on a filtering, a wavelettransformation (WO-A-00/36551), or a frequency analysis (WO-A-00/30525).

[0012] All these techniques have the common disadvantage of being slow,especially if they are implemented in miniaturized products where theprocessing capacity is limited (electronic key, for example). “Slow”means that they are poorly compatible with a real time processing ofimages taken at a rate greater than 10 images per second. The need forrapidity is, in on-board applications, linked to the need foridentification or authentication rapidity of a user by its iris, wherethe selection of a clear image thereof is a previous step.

[0013] Another disadvantage is the complexity in terms of size of theprogram necessary to execute the definition evaluation algorithm.

[0014] Another problem is, to save time and complexity of the method, tolimit the area to be examined in definition. In particular, the smallfield depth associated to the fact that the eye is spherical and thatelements such as eyelashes may be included in the image makes this arealocalization important to evaluate the definition of the iris and notthat of other image areas.

[0015] Another problem which is posed for the definition determinationof iris images, or more generally of a specific area of an image takenwith a small field depth and acquired at small distance, is linked tothe presence of areas external to the area to be evaluated (for example,eyelashes), which may be clear while the iris is not. This problem isespecially present in operators or algorithms taking into accountluminosity gradients, which amounts to taking more account of thecontours than of the actual areas. In particular, this is a disadvantageof a conventional operator or algorithm known as an FSWM operator whichis besides known as an operator providing acceptable results.

[0016] Another problem which is also posed for the definition evaluationof image areas taken at small distance and with a small field depth islinked to the necessary illumination of the taken subject. For eye imagesensors, it generally is a light-emitting diode. This light sourcecreates specular spots which pollute the definition evaluation. Inparticular, the FSWN operator mentioned hereabove may be deceived by thepresence of specular spots which tend to mask luminosity gradientsoriginating from the iris with more significant gradients originatingfrom the spots.

SUMMARY OF THE INVENTION

[0017] The present invention aims at providing a digital imageprocessing method and system which overcomes one or several of thedisadvantages of known methods.

[0018] More specifically, the present invention aims at evaluating thedefinition of an iris of an eye or the like.

[0019] The present invention also aims at selecting, from among a set ofeye images or the like, that which is the clearest.

[0020] The present invention also aims at providing a simplified methodof localization of an iris or the like in a digital eye image which issimple and consumes few calculation resources.

[0021] The present invention independently aims at enabling approximatelocalization of a pupil or the like in a digital image in a simple, fastfashion, consuming few calculation resources.

[0022] The present invention independently aims at determining a scorecharacteristic of the definition of a digital image area comprisingspecular spots.

[0023] The present invention also aims at making a luminosity gradientanalysis operator insensitive to the presence of parasitic contours inthe area having its definition evaluated.

[0024] To achieve these and other objects, the present inventionprovides a method for selecting an eye image from a set of digitalimages based on its definition, consisting, for each image in the set,of:

[0025] calculating a first approximate characteristic definition scorebased on a cumulating of the gradients in a single direction of thelight intensities of the image pixels;

[0026] selecting a subset of images for which said first score isgreater than a predetermined threshold; and

[0027] for each of the images of said subset, calculating a second scorecharacteristic of the image definition by an evaluation methodcomprising the successive steps of:

[0028] approximately localizing the pupil in the image;

[0029] defining, from the approximate position of the pupil, anexamination window centered on this position; and

[0030] applying a gradient cumulating operation to the luminance valuesof the pixels of the examination window, the running total beingproportional to the definition score of the image.

[0031] According to an embodiment of the present invention, theexamination window has an elongated shape, preferably, rectangular.

[0032] According to an embodiment of the present invention, the smallestdimension of said examination window approximately corresponds to theaverage diameter expected for the pupil.

[0033] According to an embodiment of the present invention, the largestdimension of said examination window approximately corresponds to theaverage diameter expected for the iris.

[0034] According to an embodiment of the present invention, theapproximate localization comprises the steps of:

[0035] dividing the image into blocks of identical dimensions, the sizeof which is chosen according to the approximate expected size of thepupil to be localized;

[0036] calculating, for each block, the average luminance; and

[0037] searching that of the blocks having the smallest luminance, theapproximate position of the pupil in the image corresponding to theposition of the block of minimum luminance.

[0038] According to an embodiment of the present invention, the blocksoverlap, the pitch in both directions between two neighboring blocksranging between one tenth and three quarters of the size of a block.

[0039] According to an embodiment of the present invention, the divisionis performed on a sub-sampled image of the digital image, the pitchbetween two neighboring blocks depending on the image sub-samplingratio.

[0040] According to an embodiment of the present invention, thelocalization is applied to a digital image reduced in size with respectto the original image, by eliminating two lateral strips ofpredetermined width.

[0041] According to an embodiment of the present invention, saidoperator cumulates the quadratic norm of horizontal and verticalgradients of luminance values of image pixels, the pixels being selectedat least according to a first maximum luminance threshold of otherpixels in the involved direction.

[0042] According to an embodiment of the present invention, said scoreis obtained by dividing the running total by the number of cumulatedquadratic norms.

[0043] According to an embodiment of the present invention, a currentpixel having a vertical or horizontal gradient to be taken into accountin the running total is selected only if the luminances of two pixelssurrounding the current pixel while being distant therefrom by apredetermined interval in the involved vertical or horizontal directionare smaller than said first luminance threshold, said first thresholdbeing selected according to the expected luminosity of possible specularspots which are desired not to be taken into account, and said intervalbeing selected according to the expected size of the possible specularspots.

[0044] According to an embodiment of the present invention, thequadratic norm of a gradient is taken into account in the running totalonly if its value is smaller than a predetermined gradient threshold,selected according to the image contrast.

[0045] According to an embodiment of the present invention, a currentpixel is selected to be taken into account in the running total only ifits luminance is smaller than a second luminance threshold, chosen to begreater than the expected light intensity of the iris in the image.

[0046] According to an embodiment of the present invention, the secondscore assigned to each image is used to select the clearest image fromsaid set.

[0047] The present invention also provides a digital image processingsystem.

[0048] The foregoing objects, features, and advantages of the presentinvention will be discussed in detail in the following non-limitingdescription of specific embodiments in connection with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0049]FIG. 1 very schematically shows in the form of blocks an exampleof an iris recognition system to which the present invention applies;

[0050]FIG. 2 illustrates, in the form of blocks, an embodiment of themethod for determining the score characteristic of the definition of aniris image according to the present invention;

[0051]FIG. 3 illustrates, in the form of blocks, an embodiment of theiris localization method according to the present invention; and

[0052]FIG. 4 illustrates, in the form of blocks, an embodiment of themethod for calculating the score characteristic of the definition bysearching weighted gradients according to the present invention.

DETAILED DESCRIPTION

[0053] For clarity, only those elements and those steps that arenecessary to the understanding of the present invention have been shownin the drawings and will be described hereafter. In particular, thestructure of an iris recognition system has not been detailed, thepresent invention being implementable based on a conventional system,provided that said system can be programmed to implement the presentinvention.

[0054] The present invention will be described hereafter in relationwith the selection of the clearest iris image among a set of images.However, the present invention more generally applies to thedetermination of the definition of digital images or image portionsexhibiting the same characteristics as an iris image and, especially, ofimages in which a first plane, the definition of which is desired to bedetermined, is at a different distance from a background. Further,although the present invention is described in relation with a completeexample of a definition determination method, some phases of this methodmay be implemented separately and are, alone, characteristic.

[0055]FIG. 1 very schematically shows an example of an iris recognitionsystem that can implement the selection method according to the presentinvention.

[0056] Such a system is intended to exploit eye images to perform anidentification or authentication by iridian recognition. For example, adigital sensor 1 takes a set of images of an eye O of a subject. Thenumber of images taken is generally of at least some ten images toenable performing the identification, after selection of the clearestimage, while minimizing the risk of having to ask the subject to submithimself to a new series of shootings. As an alternative, the images tobe analyzed originate from a distant source and may be pre-recorded.

[0057] Sensor 1 is connected to a CPU 2 having the function, inparticular, of implementing the actual iris recognition (block IR) afterhaving selected (block IS), from among the set of images stored in amemory 3, the clearest image IN to be submitted to the recognitionmethod. The selection method is based on the determination, for eachimage in the set, of a score characteristic of its definition. Thisdetermination is, according to the present invention, performed by meansof the method of which a preferred embodiment will be described inrelation with FIG. 2. CPU 2 is also used to control all the systemcomponents and, in particular, sensor 1 and memory 3.

[0058]FIG. 2 schematically illustrates in the form of blocks a preferredembodiment of the definition determination method according to thepresent invention.

[0059] The method of FIG. 2 comprises three separate characteristicsteps which will be described successively in relation with theprocessing of an image of the set to be evaluated, knowing that allimages in the set are processed, preferably successively, by thismethod. The selection of the image to which the highest score has beenassigned is performed, for example, by simple comparison of the assigneddefinition scores, by means of a maximum score search step, conventionalper se.

[0060] A first preprocessing phase (block 4, Pre-focus) aims ateliminating very blurred images (more specifically, of assigning a zerodefinition score) which will obviously be inappropriate for the irisrecognition. According to the present invention, this phase searchesstrong luminance gradients in the horizontal direction (arbitrarilycorresponding to the general direction of the eyelids). Such gradientsare linked to the presence of eyelashes, of abrupt grey leveltransitions between the pupil and the iris, between the iris and thewhite of the eye, between the white of the eye and the eyelid corner,etc. The more abrupt transitions there are, the clearer the image willbe. Since a rough preprocessing is here to be made, the gradient searchis preferably performed on an approximate image, that is, sub-sampled.

[0061]FIG. 3 schematically illustrates in the form of blocks anembodiment of preprocessing phase 4.

[0062] Original image I is first sub-sampled (block 41, Bidir Sampling)in both directions, preferably with a same factor. For example, thesub-sampling ratio is 4 in both directions, which amounts toapproximating the image with a factor 16.

[0063] Image SEI resulting from step 41 is then submitted to a filtering(block 42, Horiz Sobel Filtering) in a single direction, preferablyhorizontal to correspond to the direction of the main image lines. Thefiltering aims at calculating the horizontal gradient at each pixel, andthus of detecting the vertical contours.

[0064] For example, it may be a unidirectional filtering known as the“Sobel” filtering. Such a filtering operator is described, for example,in work “Analyse d'images: filtrage et segmentation“by J- P. Cocquerezet S. Phillip, published in 1995 by Masson (ISBN 2-225-84923-4).

[0065] The image resulting from the filtering is then submitted to anoperator (block 43, AF Compute) for computing the approximate definitionscore AF. In a simplified manner, this operator only calculates the sumof the intensities of the pixels of the filtered image. The higher theAF score, the clearer the image.

[0066] Score AF calculated by block 4 is compared (block 44, FIG. 2,AF>TH) with a predetermined definition threshold TH. If the obtainedscore is greater than the threshold, the definition determinationprocess carries on with a second iris centering phase which will bedescribed hereafter in relation with FIG. 4. If not, the image isrejected (block 45, Score=0) because not clear enough.

[0067] Second phase 5 (Pupil Localization) consists of locating the eyepupil in the image to center the pupil (and thus the iris) in an imageto be analyzed. This localization pursues several aims. A first aim isto subsequently concentrate the definition evaluation on the significantarea. A second aim is to avoid for areas of the image with a stronggradient (especially eyelashes), which are not in the same plane as theiris, to be taken into account in the definition evaluation, and to thencorrupt this evaluation.

[0068] Several localization methods may be envisaged. For example, amethod based on a Hough transform associated with integral anddifferential operators, described in article “Person identificationtechnique using human iris recognition” by C. Tisse, L. Martin, L.Torres, and M. Robert, published on Calgary Conference VI'02 in May2002, provides high performances.

[0069] However, it has a high resource consumption and its executiontime is thus not necessarily compatible with a real time processing.Further, for an evaluation of the definition, only an approximatelocalization is required.

[0070]FIG. 4 schematically illustrates in the form of blocks a preferredembodiment of the pupil localization phase according to the presentinvention.

[0071] Starting from original image I, lateral strips are firsteliminated from this image (block 51, Vertical Cut). This eliminationaims at not taking into account, subsequently, the dark edges (delimitedby lines T on image I) of the image on its sides. If the eye is properlycentered in the image, these strips result from the eye curvature whichcauses a lesser lighting of the edges. The size (width) of theeliminated strips depends on the resolution and on the size of theoriginal image. Each strip is, for example, of a width ranging betweenone twentieth and one fifth of the image width.

[0072] The obtained reduced image RI is then optionally submitted to asub-sampling (block 52, Bidir Sampling) in both directions. For example,the sub-sampling is performed with the same ratio as for thepreprocessing phase described in relation with FIG. 3.

[0073] The average luminance of blocks of the sub-sampled reduced imageSERI is then calculated (block 53, Mean Lum Block), the size of a blockapproximately corresponding to the expected size of the pupil in anevaluated image. This size is perfectly determinable since the processedimages are generally taken while respecting a given distance rangebetween the sensor and the eye.

[0074] The computation is performed by displacing a computation windowwith a pitch smaller than the size of a block. The blocks overlap, thepitch in both directions between two neighboring blocks ranging,preferably, between one tenth and three quarters of the size of a block.

[0075] For example, for images of 644*484 pixels in which the pupils fitwithin surfaces between approximately 50*50 pixels and approximately70*70 pixels, the luminance is calculated for blocks of 15*15 pixels(with a sub-sampling factor of 4 in each direction) by scanning theimage with a displacement of the calculation window of from 2 to 5pixels each time. An image LI of luminance values of the differentblocks is then obtained.

[0076] In this image, the block having the minimum luminance is searched(block 54, Min Lum Search). This block approximately corresponds to thatcontaining the pupil (or most of the pupil). Indeed, the pupil is thedarkest region.

[0077] In the case where the sub-sampling is omitted, the number ofblocks of which the average luminance must be calculated is higher. Thedisplacement pitch of the calculation window is however reduced (forexample, every 8 to 20 pixels).

[0078] Once the pupil has been approximately localized by its Cartesiancoordinates (X, Y) in the image (block 55, FIG. 2), it is returned tothe original image I to extract therefrom (block 56, Extract) anelongated image EI having the shape of a horizontal strip centered onthe approximate position of the pupil and of a height corresponding tothe average expected diameter of a pupil at the scale of the evaluatedimages. The fact that the entire iris is not reproduced in this imageportion is here not disturbing. Indeed, this is not an analysis of theiris for its recognition but only an evaluation of its definition. Thisdefinition will be at least approximately the same over the entire pupilperiphery and an analysis in a reduced strip containing the iris oneither side of the pupil is enough.

[0079] The elongated shape of the selected strip enables taking intoaccount the fact that the eye is often partly closed on a shooting. Thisthen enables minimizing non-relevant contours (eyelashes, eyelids).

[0080] Although an elongated rectangular image forming the definitionexamination window is the preferred embodiment, it is not excluded toprovide an oval, or even square or round examination window. In the caseof a square or round examination window, it will then be ascertained tosize it to contain, around the pupil, a sufficient iris area for thedefinition evaluation. This area will however have to be preferentiallydeprived of contours such as those of eyelids, for example, by makingsure that the eye is wide open in the image shooting.

[0081] The assigning of a score characteristic of the definition to theimage is then performed, according to the present invention, in a thirdphase (block 6, FSWM), based on elongated image EI, resulting from theprevious step.

[0082] According to the present invention, an operator of improved FSWMtype is implemented to process the images likely to contain specularspots.

[0083] In fact, an FSWM operator calculates, for all the image pixels(here elongated image EI), the sum of the quadratic norm of thehorizontal and vertical gradients of luminance value medians. Thisamounts to applying the following formula:${{\sum\limits_{{i = 0},{j = 0}}^{{i = n},{j = m}}\quad ( {{gradV}( {i,j} )} )^{2}} + ( {{gradH}( {i,j} )} )^{2}},$

[0084] with:

gradV(i,j)=Med[Lum(i,j),Lum(i+1,j),Lum(i+2,j)]−Med[Lum(i,j),Lum(i−1,j),Lum(i−2,j)],and

gradH(i,j)=Med[Lum(i,j),Lum(i,j+1),Lum(i,j+2)]−Med[Lum(i,j),Lum(i,j−1),Lum(i,j−2)],

[0085] where Lum(i,j) represents the light intensity of the pixel ofcoordinates (i,j) in image EI of size n*m and where Med designates themedian function, that is, the result of which corresponds to the medianvalue of the luminances of the pixels in the set where the function isapplied.

[0086] An FSWM operator such as described hereabove is discussed, forexample, in article “New autofocusing technique using the frequencyselective weighted median filter for video cameras” by K. S. Choi, J. S.Lee, and S. J. Ko, published in IEEE Trans. On Consumer Electronics,Vol. 45, N^(o)3, August 1999.

[0087] According to the present invention, the sum is not calculatedover all the image pixels, but is limited to some pixels chosen in thefollowing characteristic manner.

[0088] For the quadratic norm of a gradient of the median of an imagepixel to be taken into account in the sum providing the definitionscore, the respective light intensities of the pixels at a givenpredetermined distance from the pixel, the gradients of which arecalculated, must according to the present invention at least be smallerthan a first predetermined luminance threshold. This amounts to nottaking into account (not accumulating in the summing equation of theFSWM operator) the vertical gradients of the pixels of coordinates (i,j)for which Lum(i,j+k)>SAT1, or Lum(i,j−k)>SAT1, and the horizontalgradients of the pixels for which Lum(i+k,j)>SAT1, or Lum(i−k,j)>SAT1.Number k (for example, between 2 and 10) is selected according to theimage resolution to correspond to the average size of the transitionbetween a specular spot and the iris. Threshold SAT1 is chosen tocorrespond to the level of grey for which the image is considered to besaturated.

[0089] The above condition enables eliminating the pixels belonging to atransition between a possible specular spot present in image EI and therest of the eye. The pixels bringing non-relevant gradients are thus nottaken into account for the determination of the definition score.

[0090] Preferably, an additional condition is that the horizontal orvertical gradients must be, in absolute value, smaller than a gradientthreshold GTH. In the iris, gradients are relatively small. However,this enables not taking into account gradients especially originatingfrom eyelashes. The determination of threshold GTH depends on the imagecontrast and must be smaller than the average of the expected gradientsfor eyelashes.

[0091] Preferably, the light intensity of the pixel must be smaller thana second predetermined luminance threshold SAT2. Threshold SAT2 ischosen to be greater than the light intensity expected for the iris,which is generally relatively dark (especially as compared to the whiteof the eye).

[0092] As an alternative, the quadratic norm of the gradients isdirectly compared with threshold GTH (then chosen accordingly).Performing the test on the gradient before squaring it up howeverenables saving calculation time for all the eliminated gradients.

[0093] The compliance with all the above conditions corresponds to apreferred embodiment which can be expressed as follows in an algorithmicdescription.

[0094] Sc=0, NbPix=0

[0095] For all the pixels of recentered elongated image EI scanned, forexample, in a line scanning (j from 1 to m, for each i from 1 to n):

If[Lum(i,j+k)<SAT 1 AND Lum(i,j−k)<SAT 1 AND Lum(i,j)<SAT 2 AND|GradV(i,j)|<GTH], then Sc=Sc+(GradV(i,j))2 and NbPix=NbPix+1;

If[Lum(i+k,j)<SAT 1 AND Lum(i−k,j)<SAT 1 AND Lum(i,j)<SAT 2 AND|GradH(i,j)|<GTH], then Sc=Sc+(GradH(i,j))2 and NbPix=NbPix+1;

[0096] next j;

[0097] next i.

[0098] Once all pixels have been processed, the definition scoreassigned to the image is computed as being:

[0099] Score=Sc/NbPix.

[0100] This weighting enables making the indexes of the different imagessubsequently comparable to one another.

[0101] Preferably, in the application of the above operator, thevertical and horizontal gradients are, even for conditional tests withrespect to threshold GTH, only preferentially calculated if the firstthree conditions (Lum(i+k,j)<SAT1 AND Lum(i−k,j)<SAT1 AND Lum(i,j)<SAT2)relative to light intensities are verified.

[0102] It can thus be seen that many gradients are not taken intoaccount in the sum providing the score, and are not even calculated. Anadvantage then is a considerable time gain for the determination of theimage definition score.

[0103] Another advantage is that possible specular spots no longerpollute the image definition evaluation.

[0104] More generally, the present invention minimizes the number ofcomputations to be performed on the pixels of an image, the definitionof which is desired to be determined.

[0105] Another advantage of the present invention is that, as comparedto an equivalent tool implementing conventional definition calculationmethods, the present invention is faster to determine the scorescharacteristic of the definition of an image set.

[0106] Another advantage of the present invention is that, whilesimplifying and making digital processings applied to the images faster,it is more reliable than known methods as concerns the definitionevaluation.

[0107] It should be reminded that although the present invention hasbeen described in relation with the selection of an image in which theiris is the clearest among a set of digital images of an eye, it moregenerally applies to images analogous in form and/or in characteristics.Further, some phases characteristic of the discussed method may findapplications without being included in the general process and solvespecific problems, likely to arise in other processes.

[0108] In particular, the pupil localization in an eye image hasspecific advantages and enables, alone, solving problems anddisadvantages of other localization processes used in other methods andespecially in actual identification and authentication methods. Anotherexample of application relates to the detection of eye movements of aperson in animated images (gaze tracking). Here again, the rapidity withwhich the present invention enables approximate localization iscompatible with the real time processing of animated images.

[0109] Further, the phase of determination of the actual definitionscore, in that it simplifies a known FSWM operator, may find otherapplications in methods of analysis of various textures for whichsimilar problems are posed and especially, when very bright reflectionsare desired not to be taken into account. In such applications, a methodfor determining the score characteristic of the definition of an imageexhibits characteristics independent from the other phases described, asan example of application, in the present description.

[0110] Of course, the present invention is likely to have variousalterations, modifications, and improvements which will readily occur tothose skilled in the art. In particular, its implementation in softwarefashion by using known tools is within the abilities of those skilled inthe art based on the functional indications given hereabove. Further,the thresholds, block sizes, reduction or sub-sampling factors, etc.will be chosen according to the application and to the type of images ofwhich the definition is desired to be determined, and theirdetermination is within the abilities of those skilled in the art.

[0111] Such alterations, modifications, and improvements are intended tobe part of this disclosure, and are intended to be within the spirit andthe scope of the present invention. Accordingly, the foregoingdescription is by way of example only and is not intended to belimiting. The present invention is limited only as defined in thefollowing claims and the equivalents thereto.

What is claimed is:
 1. A method for selecting an eye image from a set ofdigital images based on its definition, consisting, for each image inthe set, of: calculating (4) a first approximate characteristicdefinition score (AF) based on a cumulating of the gradients in a singledirection of the light intensities of the image pixels; selecting asubset of images for which said first score is greater than apredetermined threshold; and for each of the images of said subset,calculating a second score characteristic of the image definition by anevaluation method comprising the successive steps of: approximatelylocalizing (5) the pupil in the image; defining (56), from theapproximate position of the pupil, an examination window (EI) centeredon this position; and applying (6) a gradient accumulation operation tothe luminance values of the pixels of the examination window, therunning total being proportional to the definition score of the image.2. The method of claim 1, wherein the examination window has anelongated shape, preferably, rectangular.
 3. The method of claim 2,wherein the smallest dimension of said examination window approximatelycorresponds to the average diameter expected for the pupil.
 4. Themethod of claim 1, wherein the largest dimension of said examinationwindow approximately corresponds to the average diameter expected forthe iris.
 5. The method of claim 1, wherein the approximate localizationcomprises the steps of: dividing the image into blocks of identicaldimensions, the size of which is chosen according to the approximateexpected size of the pupil to be localized; calculating, for each block,the average luminance; and searching that of the blocks having thesmallest luminance, the approximate position of the pupil in the imagecorresponding to the position of the block of minimum luminance.
 6. Themethod of claim 5, wherein the blocks overlap, the pitch in bothdirections between two neighboring blocks ranging between one tenth andthree quarters of the size of a block.
 7. The method of claim 5, whereinthe division is performed on a sub-sampled image of the digital image,the pitch between two neighboring blocks depending on the imagesub-sampling ratio.
 8. The method of claim 5, wherein the localizationis applied to a digital image reduced in size with respect to theoriginal image, by eliminating two lateral strips of predeterminedwidth.
 9. The method of claim 1, wherein said operator cumulates thequadratic norm of horizontal and vertical gradients of luminance valuesof image pixels, the pixels being selected at least according to a firstmaximum luminance threshold of other pixels in the involved direction.10. The method of claim 9, wherein said score is obtained by dividingthe running total by the number of cumulated quadratic norms.
 11. Themethod of claim 9, consisting of selecting a current pixel having avertical or horizontal gradient to be taken into account in the totalonly if the luminances of two pixels surrounding the current pixel whilebeing distant therefrom by a predetermined interval in the involvedvertical or horizontal direction are smaller than said first luminancethreshold, said first threshold being selected according to the expectedluminosity of possible specular spots which are desired not to be takeninto account, and said interval being selected according to the expectedsize of the possible specular spots.
 12. The method of claim 9, whereinthe quadratic norm of a gradient is taken into account in the total onlyif its value is smaller than a predetermined gradient threshold,selected according to the image contrast.
 13. The method of claim 9,wherein a current pixel is selected to be taken into account in thetotal only if its luminance is smaller than a second luminancethreshold, chosen to be greater than the expected light intensity of theiris in the image.
 14. The method of claim 1, wherein the second scoreassigned to each image is used to select the clearest image from saidset.
 15. A digital image processing system, comprising means forimplementing the selection method of any of claims 1 to 14.